37th International Conference on Machine Learning
The Art of Learning with Missing Values
July 17, 2020
Imputation is a popular technique for handling missing data. Variance estimation after imputation is an important practical problem in statistics. In this paper, we consider variance estimation of the imputed mean estimator under the kernel ridge regression imputation. We consider a linearization approach which employs the covariate balancing idea to estimate the inverse of propensity scores. The statistical guarantee of our proposed variance estimation is studied when a Sobolev space is utilized to do the imputation, where n-consistency can be obtained. Synthetic data experiments are presented to conﬁrm our theory.
Wang, Hengfang and Kim, Jae Kwang, "Variance estimation after Kernel Ridge Regression Imputation" (2020). Statistics Conference Proceedings, Presentations and Posters. 15.